Frequency division multiplexing (FDM) is a technology that transmits multiple signals simultaneously over a single transmission path, such as a cable or wireless system. Each signal travels within its own unique frequency range (carrier), which is modulated by the data (text, voice, video, etc.).
An orthogonal FDM (OFDM) spread spectrum technique distributes the data over a large number of carriers that are spaced apart at defined frequencies. This spacing provides the “orthogonality” of the OFDM approach, and prevents the demodulators from seeing frequencies other than their own. The benefits of OFDM include high spectral efficiency, resiliency to RF interference, and lower multipath distortion. This is useful because in a typical terrestrial wireless communications implementation there are multipath channels (i.e., the transmitted signal arrives at the receiver using various paths of different length). Since multiple versions of the signal interfere with each other (inter-symbol interference (ISI)), it becomes difficult to extract the original information.
OFDM has been successfully deployed in indoor wireless LAN and outdoor broadcasting applications. OFDM beneficially reduces the influence of ISI with a complexity that is less than that of typical single carrier adaptive equalizers. OFDM has also been found to work well in multipath fading channels. These and other advantages render OFDM a strong candidate for use in future mobile communication systems, such as one being referred to as 4G (fourth generation).
In a frequency selective fading channel each sub-carrier is attenuated individually. The resultant sub-channel frequency functions are frequency-variant and may also be time-variant, i.e. the channel magnitude may be highly fluctuating across the sub-carriers and may vary from symbol to symbol. Hence, adaptive modulation may be used to advantage to improve the error performance and data throughput (TP) in an OFDM modem (modulator/demodulator) by assigning different modulation and coding schemes to different sub-carriers.
However, one fundamental issue in deploying adaptive modulation is to determine what modulation and coding scheme (MCS) to use. For a system with several pre-defined MCS available, the problem may be viewed as the determination of switching thresholds, i.e., when to switch from using one MCS to using another MCS. Virtually all past investigations into this problem that are known to the inventors were based on heuristic methods, or employed limited analytical resources, usually under un-coded conditions.
One approach from the literature is a so-called “target BER approach”, as described by H. Rohling and R. Grunheid, “Performance of an OFDM-TDMA Mobile Communication System”, IEEE 46th Vehicular Technology Conference, Apr. 28 to May 1, 1996, Volume 3, pp. 1589–1593; and A. Czylwik, “Adaptive OFDM for Wideband Radio Channels”, IEEE GLOBECOM 96, Nov. 18–22, 1996, Volume 1, pp. 713–718. In the target BER approach the thresholds are set to be the signal-to-noise ratios (SNRs) needed for the given modulation and coding schemes in order to meet a target BER. While this approach may insure that a target BER is achieved, but does not maximize the data throughput. Another prior art method treats the issue as a parameter optimization problem and employs analytical optimization techniques (see, for example, B. S. Krongold, K. Ramchandran and D. L. Jones, “Computationally Efficient Optimal Power Allocation Algorithms for Multicarrier Communication Systems”, IEEE Trans. on Communications, Vol.48, No. 1, 2000, pp. 23–27). In this approach one would typically seek to maximize the data rate (bits/OFDM symbol) subject to a BER/SER bound and other constraints (e.g. power). However, this approach does not necessarily mean that the net throughput is optimized, especially in a packet-based system. Moreover, this approach is tailored for a specific modulation scheme, channel condition and operating constraints, and needs to be re-evaluated if any one of them changes.
Discussing these prior art approaches now in further detail, in the “targeted BER approach” the thresholds are derived from the BER curves under AWGN. In such an approach a set of Gaussian BER curves for the available MCSs is plotted, and the SNR thresholds are read from the graph for a target BER. While this approach may insure a certain maximum tolerable BER, it has no control over the resultant throughput, which may be a more important performance criterion in some applications, e.g., when downloading files. Variants on the targeted BER approach are also available, for example the thresholds may be shifted according to the mean SNR across a block of sub-carriers (see, for example, R. Grunheid, E. Bolinth and H. Rohling, “A Blockwise Loading Algorithm for the Adaptive Modulation Technique in OFDM Systems”, IEEE 54th Vehicular Technology Conference, October 2001, Volume 2, pp. 948–951), or one may estimate the overall BER for all available modulation schemes in a group of sub-carriers and select the scheme that gives the highest throughput while also satisfying a BER bound (see, for example, T. Keller and L. Hanzo, “Adaptive Modulation Techniques for Duplex OFDM Transmission”, IEEE Trans. on Vehicular Technology, Vol. 49, No. 5, September 2000, pp.1893–1906), or one may adjust the power of the individual sub-carriers to reduce the excessive margin (see, for example, T. Yoshiki, S. Sampei and N. Morinaga, “High Bit Rate Transmission Scheme with a Multilevel Transmit Power Control for the OFDM based Adaptive Modulation Systems”, IEEE 53rd Vehicular Technology Conference, May 200 1, Volume 1, pp. 727–731).
The other technique, i.e., the “parameter optimization approach”, formulates the modulation selection issue as a parameter optimization problem. The aim is to optimize the rate (bits/symbol) subject to a number of constraints. For instance, Krongold et al. (B. S. Krongold, K. Ramchandran and D. L. Jones, “Computationally Efficient Optimal Power Allocation Algorithms for Multicarrier Communication Systems”, IEEE Trans. on Communications, Vol.48, No. 1,2000, pp.23–27) proposed a Lagrange bisection solution that maximizes the rate (bits/symbol) subject to a total power constraint and a fixed error probability bound. An additional practical constraint is that the rate should be an integer number of bits/symbol. Unfortunately, channel coding, which is frequently employed to combat fading, may be difficult to incorporate in such an analytical approach. A certain channel distribution is also often assumed, in other words the derived solution only works for a given channel condition and should be re-evaluated when the channel changes. Moreover, in a packet-data based system with channel coding, it may be more desirable to maximize the net data throughput, defined as (1-PER)*data_rate, where data_rate is the actual data rate in packets/symbols per time unit (or other normalized values), rather than the raw data rate, and PER is the Packet Error Rate. However this is difficult to perform analytically. In fact, little or no literature is available that deals with packet errors and the associated optimization of throughput for a coded OFDM system.
In general, analytical modeling is basically inaccurate, and may at best be simply an approximation of many practical operating conditions. The heuristic method is often subjective, represents but one of the many solutions available, and may not provide the most optimal performance.
Based on the foregoing, it should be appreciated the problem of optimally making adjustments of MCS switching thresholds in an adaptive OFDM modem, to improve or maximize data throughput, has not been adequately resolved.